Summary of Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation, By Md Fahim Sikder et al.
Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
by Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to generating fair synthetic data using knowledge distillation. The method, called Fair Latent Space Distillation (FLSD), aims to mitigate biases in AI-powered applications by learning a syntax-agnostic representation of the data and then distilling it into a smaller model. This allows for more flexible and stable training of Fair Generative Models (FGMs). The approach combines quality loss for fair distillation and utility loss for data utility, achieving a 5%, 5%, and 10% rise in performance for fairness, synthetic sample quality, and data utility, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make AI models more fair. Right now, many AI models can reflect the biases they were trained on, which is a big problem. The researchers propose using a smaller model to learn how to make data fairer, so that bigger models don’t have to do all the work. This makes the training process more stable and efficient. The new method shows great results in making synthetic data that is both useful and unbiased. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation » Latent space » Syntax » Synthetic data
